Chapter 8 · Product Analyst
8. Tools, instrumentation & data quality
~6 min read
8.1 The product analytics toolstack#
| Category | Common tools | What it's for |
|---|---|---|
| Product analytics | Amplitude, Mixpanel, Heap | Funnels, retention, behavioral cohorts |
| SQL / warehouse | BigQuery, Snowflake, Redshift | Ad-hoc analysis at scale |
| BI / dashboards | Looker, Tableau, Power BI | Self-serve metrics for the team |
| Experimentation | Statsig, Optimizely, in-house | Running and reading A/B tests |
| Tracking / CDP | Segment, RudderStack | Collecting and routing event data |
8.2 Event instrumentation#
A good tracking plan defines each event's name, when it fires, and its properties, before code ships. When events are named inconsistently, fire twice, or miss properties, your funnels and retention quietly lie.
8.3 Data-quality checklist#
- Volume sanity: do event counts match expectations and prior baselines?
- Duplicate events: is a single action firing more than once?
- Null / missing properties: are key properties populated for every event?
- Reconciliation: does revenue/users tie out to a trusted source?
- Post-release checks: after every deploy, confirm core events still fire as expected.
8.4 Designing an event taxonomy#
A widely used pattern is Object + Action, e.g. Video Played, Cart Updated, Checkout Completed. Consistency matters more than cleverness.
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